Senior AI Engineer (LLMs & Generative AI)
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Monthly Salary:
Experience Required:
Posted on:
7 hours ago
Vacancies:
1 Vacancy
Job Summary
Location - Medellín Colombia Hybrid / Onsite / Flexible
Role Overview
We are seeking a Senior AI Engineer (LLMs & Generative AI) to design build and scale enterprise-grade AI systems powered by large language models generative AI technologies and massive datasets. This role is ideal for a highly skilled engineer who has hands-on experience working with LLMs in production understands how to implement AI guardrails and responsible AI practices and has built scalable AI solutions across complex enterprise environments.
You will play a key role in shaping AI capabilities across the organization from model orchestration and retrieval systems to safety governance and performance optimization while collaborating with global teams in a fast-moving innovation-driven environment. We are especially interested in bilingual (English/Spanish) professionals who can operate effectively across technical and business teams internationally.
Key Responsibilities
LLM & Generative AI Development:
- Design develop and deploy applications powered by LLMs and generative AI models.
- Build solutions for use cases such as enterprise search document intelligence summarization conversational AI and workflow automation.
- Work with both commercial and open-source LLMs depending on use case and performance requirements.
- Optimize prompts model parameters and inference workflows for quality latency and cost.
AI Guardrails & Responsible AI:
- Design and implement AI guardrails to ensure safe reliable and policy-aligned outputs.
- Develop mechanisms for hallucination mitigation content filtering and moderation prompt injection defense output validation and verification and access and usage controls.
- Build evaluation frameworks to measure accuracy safety groundedness and consistency.
- Partner with security and compliance teams to align AI systems with enterprise governance standards.
Massive Data & RAG Systems:
- Work with large-scale structured and unstructured data to power AI systems.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
- Develop workflows for data ingestion and preprocessing chunking and embedding indexing and vector search context retrieval and ranking.
- Collaborate with data engineering teams to ensure scalability and performance across large datasets.
Model Orchestration & Evaluation:
- Implement orchestration strategies across multiple models and APIs.
- Develop fallback routing and hybrid model strategies for optimal outcomes.
- Define and track evaluation metrics for model performance.
- Conduct benchmarking A/B testing and continuous improvement of AI systems.
- Engineering & Deployment:
- Build production-ready AI systems using modern software engineering practices.
- Integrate AI capabilities into enterprise applications APIs and workflows.
- Support CI/CD pipelines versioning testing and monitoring of AI services.
- Ensure systems are scalable observable and cost-efficient.
Cross-Functional Collaboration:
- Partner with product engineering data and business teams to translate requirements into AI solutions.
- Communicate technical concepts clearly to stakeholders across global teams.
- Contribute to architecture decisions documentation and reusable AI frameworks.
- Work effectively in English-speaking environments with international stakeholders.
Requirements
Required Qualifications
- Bachelors or Masters degree in Computer Science AI Machine Learning Data Science or related field.
- 5 years of experience in software engineering AI or machine learning roles.
- Strong hands-on experience building applications using LLMs and generative AI technologies.
- Experience working with large-scale enterprise data environments.
- Proven understanding of prompt engineering RAG architectures model evaluation AI safety and guardrails.
- Strong programming skills in Python and experience with backend development.
- Ability to design and deploy production-grade AI systems.
- Fluent English communication skills (required).
- Bilingual (English/Spanish) preferred.
Preferred Technical Experience
- Experience with frameworks and tools such as LangChain LlamaIndex Semantic Kernel Hugging Face ecosystem OpenAI / Azure OpenAI / Anthropic APIs.
- Experience with vector databases (e.g. Pinecone Weaviate FAISS) embeddings and semantic search model fine-tuning or adaptation techniques and AI observability and monitoring tools.
- Familiarity with cloud platforms (AWS Azure or GCP) Databricks Spark or distributed data systems Docker Kubernetes and scalable deployment patterns MLOps pipelines and AI lifecycle management.
- Understanding of security privacy and compliance in AI systems enterprise governance and access controls.
Preferred Certifications
- Microsoft Certified: Azure AI Engineer Associate
- AWS Certified Machine Learning Specialty
- Google Professional Machine Learning Engineer
- Databricks Machine Learning Certification
- TensorFlow Developer Certificate
- Certifications in Responsible AI AI Governance or Data Engineering
What Were Looking For
- A hands-on AI engineer who can take solutions from concept to production
- Strong expertise in LLMs generative AI and enterprise-scale data systems
- Someone who understands that AI safety and guardrails are critical
- A professional who can balance innovation with reliability and governance
- A bilingual globally-minded communicator who collaborates effectively across teams
- A candidate with strong ownership curiosity and problem-solving ability
Why This Role is Attractive
- Work on cutting-edge generative AI and LLM initiatives
- Solve real-world enterprise problems with high impact
- Exposure to large-scale data modern AI architectures and global teams
- Strong growth path in one of the most in-demand fields globally
- Opportunity to shape how AI is safely and effectively deployed at scale
Required Skills:
Experience with frameworks and tools such as LangChain LlamaIndex Semantic Kernel Hugging Face ecosystem OpenAI / Azure OpenAI / Anthropic APIs. Experience with vector databases (e.g. Pinecone Weaviate FAISS) embeddings and semantic search model fine-tuning or adaptation techniques and AI observability and monitoring tools. Familiarity with cloud platforms (AWS Azure or GCP) Databricks Spark or distributed data systems Docker Kubernetes and scalable deployment patterns MLOps pipelines and AI lifecycle management. Understanding of security privacy and compliance in AI systems enterprise governance and access controls.
Required Education:
Bachelors or Masters degree in Computer Science AI Machine Learning Data Science or related field.5 years of experience in software engineering AI or machine learning hands-on experience building applications using LLMs and generative AI working with large-scale enterprise data understanding of prompt engineering RAG architectures model evaluation AI safety and programming skills in Python and
